{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 载入数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_url = (\"http://archive.ics.uci.edu/ml/machine-\"\n",
    "              \"learning-databases/abalone/abalone.data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  \n",
       "0         0.150     15  \n",
       "1         0.070      7  \n",
       "2         0.210      9  \n",
       "3         0.155     10  \n",
       "4         0.055      7  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_mapping = {\n",
    "    'Sex': '性别', \n",
    "    'Length': '长度', \n",
    "    'Diameter': '直径', \n",
    "    'Height': '高度', \n",
    "    'Whole weight': '整体重量',             \n",
    "    'Shucked weight': '去壳后重量', \n",
    "    'Viscera weight': '脏器重量', \n",
    "    'Shell weight': '壳的重量',             \n",
    "    'Rings': '环数'  # 目标变量\n",
    "}\n",
    "\n",
    "try:\n",
    "    df_abalone = pd.read_csv(\"../../data/abalone.csv\", header=0)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "    df_abalone = pd.read_csv(target_url, header=None, prefix=\"V\")\n",
    "    df_abalone.columns = columns_mapping.keys()\n",
    "    df_abalone.to_csv(\"../../data/abalone.csv\", index=False)\n",
    "\n",
    "df_abalone.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4172</th>\n",
       "      <td>F</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.8870</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>0.2490</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>M</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.9660</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>0.2145</td>\n",
       "      <td>0.2605</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>M</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1.1760</td>\n",
       "      <td>0.5255</td>\n",
       "      <td>0.2875</td>\n",
       "      <td>0.3080</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>F</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.485</td>\n",
       "      <td>0.150</td>\n",
       "      <td>1.0945</td>\n",
       "      <td>0.5310</td>\n",
       "      <td>0.2610</td>\n",
       "      <td>0.2960</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>M</td>\n",
       "      <td>0.710</td>\n",
       "      <td>0.555</td>\n",
       "      <td>0.195</td>\n",
       "      <td>1.9485</td>\n",
       "      <td>0.9455</td>\n",
       "      <td>0.3765</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "4172   F   0.565     0.450   0.165        0.8870          0.3700   \n",
       "4173   M   0.590     0.440   0.135        0.9660          0.4390   \n",
       "4174   M   0.600     0.475   0.205        1.1760          0.5255   \n",
       "4175   F   0.625     0.485   0.150        1.0945          0.5310   \n",
       "4176   M   0.710     0.555   0.195        1.9485          0.9455   \n",
       "\n",
       "      Viscera weight  Shell weight  Rings  \n",
       "4172          0.2390        0.2490     11  \n",
       "4173          0.2145        0.2605     10  \n",
       "4174          0.2875        0.3080      9  \n",
       "4175          0.2610        0.2960     10  \n",
       "4176          0.3765        0.4950     12  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_abalone.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 鲍鱼数据集的描述性统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "      <td>4177.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.523992</td>\n",
       "      <td>0.407881</td>\n",
       "      <td>0.139516</td>\n",
       "      <td>0.828742</td>\n",
       "      <td>0.359367</td>\n",
       "      <td>0.180594</td>\n",
       "      <td>0.238831</td>\n",
       "      <td>9.933684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.120093</td>\n",
       "      <td>0.099240</td>\n",
       "      <td>0.041827</td>\n",
       "      <td>0.490389</td>\n",
       "      <td>0.221963</td>\n",
       "      <td>0.109614</td>\n",
       "      <td>0.139203</td>\n",
       "      <td>3.224169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.441500</td>\n",
       "      <td>0.186000</td>\n",
       "      <td>0.093500</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.545000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.799500</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>0.234000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.615000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>1.153000</td>\n",
       "      <td>0.502000</td>\n",
       "      <td>0.253000</td>\n",
       "      <td>0.329000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>2.825500</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Length     Diameter       Height  Whole weight  Shucked weight  \\\n",
       "count  4177.000000  4177.000000  4177.000000   4177.000000     4177.000000   \n",
       "mean      0.523992     0.407881     0.139516      0.828742        0.359367   \n",
       "std       0.120093     0.099240     0.041827      0.490389        0.221963   \n",
       "min       0.075000     0.055000     0.000000      0.002000        0.001000   \n",
       "25%       0.450000     0.350000     0.115000      0.441500        0.186000   \n",
       "50%       0.545000     0.425000     0.140000      0.799500        0.336000   \n",
       "75%       0.615000     0.480000     0.165000      1.153000        0.502000   \n",
       "max       0.815000     0.650000     1.130000      2.825500        1.488000   \n",
       "\n",
       "       Viscera weight  Shell weight        Rings  \n",
       "count     4177.000000   4177.000000  4177.000000  \n",
       "mean         0.180594      0.238831     9.933684  \n",
       "std          0.109614      0.139203     3.224169  \n",
       "min          0.000500      0.001500     1.000000  \n",
       "25%          0.093500      0.130000     8.000000  \n",
       "50%          0.171000      0.234000     9.000000  \n",
       "75%          0.253000      0.329000    11.000000  \n",
       "max          0.760000      1.005000    29.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 自动忽略了第一列sex属性：因为是字符型数据\n",
    "df_abalone_summary = df_abalone.describe()  \n",
    "df_abalone_summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制箱线图（盒形图）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据是包含Rings：目标变量的\n",
    "arr = df_abalone.iloc[:, 1:9].values\n",
    "xticklabels = df_abalone.columns[1:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 直接使用采集的数据（未经处理）绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.boxplot(arr)\n",
    "ax.set_xlabel('Attribute Index')\n",
    "ax.set_ylabel('Quantile Ranges')\n",
    "ax.set_xticklabels(xticklabels)\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_fontsize(15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 剔除目标变量Rings后重新用采集的数据绘制\n",
    "取出rings的原因不是因为他是目标变量，而是他的尺度太大，导致其他变量没法获得直观的感受"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2, _ = np.hsplit(arr, [-1])\n",
    "xticklabels2 = df_abalone.columns[1:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#the last column (rings) is out of scale with the rest\n",
    "# - remove and replot\n",
    "fig = plt.figure(figsize=(16, 8))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.boxplot(arr2)\n",
    "ax.set_xlabel('Attribute Index')\n",
    "ax.set_ylabel('Quantile Ranges')\n",
    "ax.set_xticklabels(xticklabels2)\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_fontsize(15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 将所有数据标准化（z-score）后，绘制箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing is okay but renormalizing the variables generalizes better.\n",
    "#renormalize columns to zero mean and unit standard deviation\n",
    "#this is a common normalization and desirable for other operations\n",
    "# (like k-means clustering or k-nearest neighbors\n",
    "arr3 = (arr - np.mean(arr, axis=0))/np.std(arr, axis=0)\n",
    "xticklabels3 = xticklabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.boxplot(arr3)\n",
    "ax.set_xlabel('Attribute Index')\n",
    "ax.set_ylabel('Quantile Ranges - Normalized')\n",
    "ax.set_xticklabels(xticklabels3)\n",
    "for label in ax.xaxis.get_ticklabels():\n",
    "    label.set_rotation(45)\n",
    "    label.set_fontsize(15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据标准化为均值为0，标准差为1之后，并不是说所有的数据都在-1.0~1.0之间，只是盒子的顶底多少都会在1.0和-1.0附近，但还是有很多的数据在这个边界之外"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
